Quantitative Neuroscience

Lessons and code for using quantitative approaches to analyze and interpret neuroscience data.

Contents

Each folder (listed below) contains lessons that are a combination of no code (typically .ipynb or .md files in the top-level folder) plus python, matlab, and (sometimes) R code in subfolders.

Concepts

Confidence Intervals and Bootstrapping
Descriptive Statistics
Error Types, P-Values, False-Positive Risk, and Power Analyses
Frequentist vs. Bayesian Approaches
Independence and Lack Thereof
Multiple Comparisons
Parametric vs. Nonparametric Statistics
Samples and Populations
Glossary

Hypothesis Testing

ANOVA
Exact Binomial Test
Proportions
Simple Nonparametric Tests
t-Tests
Z-Test

Measures of Association

Overview
Linear Regression
Nonparametric Correlation Coefficient
Parametric Correlation Coefficient

Probability Distributions

Overview
Bernoulli Distribution
Binomial Distribution
Exponential Distribution
Gaussian (Normal) Distribution
Poisson Distribution
Student's t Distribution

Data

Nonnegative Matrix Factorization (NMF)

Machine Learning

AutoML with TPOT

For Penn NGG members

QNC syllabus, lecture notes, and readings.

Credits

Copyright 2022 by Joshua I. Gold
Neuroscience Graduate Group
University of Pennsylvania